The development of surgical techniques has made great progress over the years. For instance, patients in need of brain surgery may instead have non-invasive surgery which drastically reduces the trauma to the patients.
One system for non-invasive surgery is the Leksell Gamma Knife® Perfexion system, which provides such surgery by means of gamma radiation. The radiation is emitted from a large number of fixed radioactive sources and is focused by means of collimators, i.e. passages or channels for obtaining a beam of limited cross section, towards a defined target or treatment volume. Each of the sources provides a dose of gamma radiation which, by itself, is insufficient to damage intervening tissue. However, tissue destruction occurs where the radiation beams from a plurality of radiation sources intersect or converge, causing the radiation to reach tissue-destruc-tive levels. The point of convergence is hereinafter referred to as the “focus point”.
Treatment planning optimization inradiation therapy, including for example gamma knife radio-surgery, aims at delivering sufficiently high dose to the target volume within the patient (e.g. in treatment of tumours) at the same time as the dose delivered to adjacent normal tissue is minimized. In treatment plan optimization, at least three competing factors have to be considered: delivering a sufficiently high dose to the target volume, sparing the surrounding normal or healthy tissue and keeping the treatment time as short as possible.
The treatment plan optimization is a process including optimizing the relative isocenter locations or beam directions, the beam shape settings (e.g. collimator configuration) and the fluences. In, for example, the Leksell Gamma Knife® Perfexion system the treatment plan optimization may include optimizing number of shots being used the shot size, the shot time, and the position of the shot. The irregularity and size of a target volume greatly influence relative isocenter locations or beam directions, the beam shape settings (e.g. collimator configuration) and the fluences used to optimize the treatment.
In treatment planning, inverse treatment planning has gained more and more interest. Inverse planning generally refers to the stage in treatment planning where a deliverable treatment plan is sought, such that a number of criteria are satisfied. Inverse planning can be contrasted to forward planning, where the operator manually places, weights and shapes shots. The promises of inverse planning are shorter planning times and higher quality plans. Today, inverse planning is sometimes tightly integrated with forward planning, e.g. in the software accompanying the Gamma Knife: Leksell GammaPlan. It is based on relative isodoses and uses metrics that are well-known in radiosurgery. This facilitates the transition from forward to inverse planning, and is presumably one of the reasons for the widespread adoption of inverse planning. A downside of relative isodose-based inverse planner and the complexity of the objectives is that the resulting optimization problem is inherently difficult to solve. In realistic cases it requires a compromise between computation time and the risk of ending up in a poor local optimum. This makes it difficult to explore what trade-offs are achievable—especially in complicated cases with multiple conflicting objectives. For example, a multi-metastases case where at least one metastasis is adjacent to an organ at risk. Incidentally, in such a case it might also be desirable to specify some criteria that must be met, a capability lacking in present inverse planners.
In present inverse treatment planning for Gamma Knife radiosurgery, the relative isodoses are the fundamental object of interest. This is a heuristic motivated by the dose fall-off being the steepest for a certain isodose level, which should coincide with the target boundary. Incidentally, this is true for a single shot but need not be true when the dose distribution is the sum of contributions from multiple shots. Note that utilizing steep gradients presupposes high positional accuracy. For an isocenter the decision variables are the position, collimator configuration and beam-on time. The isocenter locations are moved during the optimization and the collimator configuration is treated as a discrete element in the set of all possible collimator configurations. Organs at risk are not handled explicitly in the objective function, which can be a severe limitation. Evidently, tolerance doses for organs at risk are given in absolute dose but in the present mode of planning, absolute dose is assigned only after completing the plan. This result a in an optimization problem that is very hard in the sense that any solution method requires either extensive computations or runs the risk of returning unsatisfactory local solutions.
Hence, there is a need of more efficient methods for planning and optimizing the treatment.